Biology subjects

Chuang, E. Y.

Publications and source records attributed to Chuang, E. Y..

3 recordsLinked to original sources

bioRxiv2020

COVID-19: Variant screening, an important step towards precision epidemiology

Precision epidemiology using genomic technologies allows for a more targeted approach to COVID-19 control and treatment at individual and population level, and is the urgent need of the day. It enables identification of patients who may be at higher risk than others to COVID-19-related mortality, due to their genetic architecture, or who might respond better to a COVID-19 treatment. The COVID-19 virus, similar to SARS-CoV, uses the ACE2 receptor for cell entry and employs the cellular serine protease TMPRSS2 for viral S protein priming. This study aspires to present a multi-omics view of how variations in the ACE2 and TMPRSS2 genes affect COVID-19 infection and disease progression in affected individuals. It reports, for both genes, several variant and gene expression analysis findings, through (i) comparison analysis over single nucleotide polymorphisms (SNPs), that may account for the difference of COVID-19 manifestations among global sub-populations; (ii) calculating prevalence of structural variations (copy number variations (CNVs) / insertions), amongst populations; and (iii) studying expression patterns stratified by gender and age, over all human tissues. This work is a good first step to be followed by additional studies and functional assays towards informed treatment decisions and improved control of the infection rate.

genomics↗

bioRxiv2020

Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public dataset

Breast cancer is a heterogeneously complex disease. A number of molecular subtypes with distinct biological features lead to different treatment responses and clinical outcomes. Traditionally, breast cancer is classified into subtypes based on gene expression profiles; these subtypes include luminal A, luminal B, basal like, HER2-enriched, and normal-like breast cancer. This molecular taxonomy, however, could only be appraised through transcriptome analyses. Our study applies deep convolutional neural networks and transfer learning from three pre-trained models, namely ResNet50, InceptionV3 and VGG16, for classifying molecular subtypes of breast cancer using TCGA-BRCA dataset. We used 20 whole slide pathological images for each breast cancer subtype. The results showed that our scale training reached about 78% of accuracy for validation. This outcomes suggested that classification of molecular subtypes of breast cancer by pathological images are feasible and could provide reliable results

bioinformatics↗

bioRxiv2019

RNASeqR: an R package for automated two-group RNA-Seq analysis workflow

RNA-Seq analysis has revolutionized researchers understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps; however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).

bioinformatics↗